Give each agent a clear job.
Define its role, inputs, deliverable, and budget. Make handoffs part of the plan.
THE WORKSPACE FOR AGENT TEAMS
Many agents.
One shared
direction.
Your agents, context, and budget.
Finally on the same page.
When the brief changes, see what needs another pass—and what’s worth keeping. Built for small teams doing big things with AI.
Interactive prototype / Claude integration planned
ONE PROJECT. CONNECTED THINKING.SAMPLE WORKFLOW
↳
Try it ↗THE BRIEF CHANGED“Let’s build this for solo developers.”
4 tasks to revisit2 artifacts to keepOne focused next step.
LESS REPEATED WORK. MORE SHARED PROGRESS.See the idea in action ↓
THE WHOLE TEAM.
THE WHOLE PICTURE.◎ Agents▧ Context⇄ Collaboration◇ Evidence◴ Budget
THE WHOLE PICTURE.◎ Agents▧ Context⇄ Collaboration◇ Evidence◴ Budget
01 / WORK CHANGES. TEAMS SHOULD ADAPT.Keep the progress.
Keep the progress.
Rethink the right parts.
For small software teams turning ideas into working products with AI. The next challenge is keeping every agent aligned as the project evolves.
Share the context that matters.
Connect work to versioned sources and upstream outputs, so changes have a traceable path.
Make the next run intentional.
Identify affected work, preserve valid artifacts, and inspect the cost before continuing.
02 / LESS TALK. EXPLORE THE PROTOTYPE.Change the brief.
Change the brief.
Follow the impact.
Change a project input and follow the impact.
Inspect an agent, adjust a budget, replay the work.
LOCAL DEMO · SAMPLE DATA · NO AI CALLS
From brief to release v1
Try a changeFollow the affected tasks below
PROJECT DEPENDENCIES
Current Needs replay
Six roles. One project.
Inspect a contract to edit its instructions and sample budget. New instructions invalidate that agent’s output and dependent work.
The sources behind the work.
Each source has a revision and direct consumers. Dependencies carry changes through to downstream outputs.
A record of the work.
Inspect source changes, replayed tasks, and the illustrative cost of each run. All numbers are synthetic.
Total sample spend this visit$0.000
Sample ready. No AI calls or real spending. LOCAL
↳ Working dependency logic. Generated records. No model outputs.See exactly what works today →
03 / THE INTELLIGENCE BEHIND THE WORKA clear role
A clear role
for Claude.
Turn an evolving brief into focused work for the right agents.
Explore the integration plan →Understand the change.
Use Claude to interpret a revised brief and propose which assumptions and tasks need attention.
Prepare the next context.
Build a focused packet of instructions, sources, and relevant artifacts for each agent.
Review the evidence.
Assess output against the task, flag unresolved claims, and connect findings to inspectable sources.
✳
Claude integration is in development planning. Today’s public demo uses explicit dependencies and local rules. It makes no API calls.
04 / A FOCUSED START. A BROADER WORKSPACE.Build the foundation.
Build the foundation.
Grow with the team.
Our first workflow is software project preparation. The longer-term workspace brings agents, shared knowledge, collaboration, and delivery into one place.
Read the full product direction ↗NOW · INTERACTIVE PROTOTYPE
See change propagate.
Versioned inputs, dependency invalidation, agent contracts, budget checks, and an exportable sample ledger.
NEXT · FIRST CLAUDE INTEGRATION
Run a real project loop.
Brief analysis, isolated agent contexts, tool execution, measured API usage, and human review.
LATER · TEAM WORKSPACE
Connect the work around you.
Repository and MCP connections, persistent artifacts, shared decisions, and collaboration across projects.
EFFICIENCY HAS TO BE EARNED.Less spend only counts
Less spend only counts
if the work still holds up.
Our next milestone is a real Claude workflow, compared against a full rerun and a simple dependency baseline.
Read the evaluation plan ↗Quality before savings
Check accepted outputs and missed changes.
Count the whole run
Include planning, execution, and review costs.
Make results inspectable
Keep task inputs, records, and failures visible.
Evaluation planned. No measured savings claimed.
A LITTLE MORE CONTEXT
Good questions.
Who is OneChorus for?+
Small software teams and independent builders working with several AI agents across research, planning, implementation, and review. We are starting with projects whose requirements change while work is underway.
Can I connect my Claude account today?+
Not yet. This is an early interactive prototype. Direct Claude API integration is planned; the demo does not request credentials, call a model, or run live agents.
Are the savings in the demo real?+
The dependency decisions are computed by working code, but the tasks and costs are illustrative fixtures. They are not measured API results or a promise of savings. A live evaluation will include planning overhead, task quality, latency, and total cost.
How is valid work identified?+
The demo compares saved input revisions, upstream artifact revisions, and agent instruction revisions. Changes invalidate downstream work. This only establishes freshness within the declared graph. Unknown dependencies require a full replay, and semantic correctness still needs independent checks.
What data does this prototype collect?+
No accounts, uploads, analytics, or form submissions are included. Edits stay in the current tab and disappear on refresh. The hosting provider may keep ordinary access logs. JSON downloads contain only the sample and your local edits.
A shared direction.
A more deliberate AI team.
Try changing the brief ↗Start with a sample. Follow the work.
Have an agent workflow in mind? Let’s talk.
nakyuz@onechorus.stream